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I have a dataset of different products and their possible configurations. I want to build a model which can predict the next part for the product given the previous part/parts. This model will be used in a configurator application, where user will select one part for the product and the next compatible part will be suggested. For example, for a laptop, if user selects an i7 processor, the RAM suggested would be 16gb, after selecting the RAM, a compatible motherboard would be suggested and so on.

The dataset looks something like this

PRODUCT_ID  COMPONENT_ID  COMPONENT_VALUE   STEP_SEQUENCE
A(laptop)   Material      Plastic           0
A           CPU           i7                1
A           RAM           16gb              2
A           HDD           1TB               4
A           Screen        15inch            6

B(Mobile)   Size          6inch             1        
B           Display       AMOLED            2
B           Band          LTE               3

A(Laptop)   RAM           8gb               1           
A           HDD           500gb             2
A           CPU           i3                5

C(Car)      Engine        v8                1
C           Seats         6                 2

The problem is that the next value to predict for a product can be based on previous one or more values. Due to this, I cannot keep a fixed window size to create a dataset that can be trained by a neural network(LSTM), As different products have different amount of components.

I cannot use simple row wise classification (Machine Learning) because the output depends on previous input.

Is there a method for multivariate time series classification where I can output value based on previous one value AND more than one values using the same model.

Is this a data preparation problem? Is it possible to even train such a model? Any help or ideas regarding this?

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